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Physical Sciences and Mathematics

Abstract

The purpose of this thesis was to use Convolutional Neural Networks (CNN) to separate muons and pions for use in increasing the acceptance rate of muons below the implemented 75cm track length cut in the Charged Current Inclusive (CC-Inclusive) event selection for the CC-Inclusive Cross-Section Measurement. In doing this, we increase acceptance rate for CC-Inclusive events below a specific momentum range.